Analysis of GLDS-120 from NASA GeneLab

This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven

Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491

1. Read data

First we set up the working directory to where the files are saved.

 setwd('~/Documents/HTML_R/GLDS120')

R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.

 if(file.exists('iDEP_core_functions.R'))
    source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).

 inputFile <- 'GLDS120_Expression.csv'
 sampleInfoFile <- 'GLDS120_Sampleinfo.csv'
 gldsMetadataFile <- 'GLDS120_Metadata.csv'
 geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
 geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db'  # pathway database in SQL; can be GMT format 
 STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

 input_missingValue <- 'geneMedian' #Missing values imputation method
 input_dataFileFormat <- 1  #1- read counts, 2 FKPM/RPKM or DNA microarray
 input_minCounts <- 0.5 #Min counts
 input_NminSamples <- 1 #Minimum number of samples 
 input_countsLogStart <- 4  #Pseudo count for log CPM
 input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr)   #  install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Col0_GC_Alight_Rep1 Col0_GC_Alight_Rep2 Col0_GC_Alight_Rep3 Col0_GC_dark_Rep1 Col0_GC_dark_Rep2 Col0_GC_dark_Rep3 Ws_GC_Alight_Rep1 Ws_GC_Alight_Rep2 Ws_GC_Alight_Rep3 Ws_GC_dark_Rep1 Ws_GC_dark_Rep2 Ws_GC_dark_Rep3 Col0PhyD_GC_Alight_Rep1 Col0PhyD_GC_Alight_Rep2 Col0PhyD_GC_Alight_Rep3 Col0PhyD_GC_dark_Rep1 Col0PhyD_GC_dark_Rep2 Col0PhyD_GC_dark_Rep3 Col0_FLT_Alight_Rep1 Col0_FLT_Alight_Rep2 Col0_FLT_Alight_Rep3 Col0_FLT_dark_Rep1 Col0_FLT_dark_Rep2 Col0_FLT_dark_Rep3 Ws_FLT_Alight_Rep1 Ws_FLT_Alight_Rep2 Ws_FLT_Alight_Rep3 Ws_FLT_dark_Rep1 Ws_FLT_dark_Rep2 Ws_FLT_dark_Rep3 Col0PhyD_FLT_Alight_Rep1 Col0PhyD_FLT_Alight_Rep2 Col0PhyD_FLT_Alight_Rep3 Col0PhyD_FLT_dark_Rep1 Col0PhyD_FLT_dark_Rep2 Col0PhyD_FLT_dark_Rep3
Sample.LongId Atha.Col.0.root.GC.Alight.Rep1.GSM2493759.RNAseq.RNAseq Atha.Col.0.root.GC.Alight.Rep2.GSM2493760.RNAseq.RNAseq Atha.Col.0.root.GC.Alight.Rep3.GSM2493761.RNAseq.RNAseq Atha.Col.0.root.GC.dark.Rep1.GSM2493768.RNAseq.RNAseq Atha.Col.0.root.GC.dark.Rep2.GSM2493769.RNAseq.RNAseq Atha.Col.0.root.GC.dark.Rep3.GSM2493770.RNAseq.RNAseq Atha.Ws.root.GC.Alight.Rep1.GSM2493762.RNAseq.RNAseq Atha.Ws.root.GC.Alight.Rep2.GSM2493763.RNAseq.RNAseq Atha.Ws.root.GC.Alight.Rep3.GSM2493764.RNAseq.RNAseq Atha.Ws.root.GC.dark.Rep1.GSM2493771.RNAseq.RNAseq Atha.Ws.root.GC.dark.Rep2.GSM2493772.RNAseq.RNAseq Atha.Ws.root.GC.dark.Rep3.GSM2493773.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.Alight.Rep1.GSM2493765.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.Alight.Rep2.GSM2493766.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.Alight.Rep3.GSM2493767.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.dark.Rep1.GSM2493774.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.dark.Rep2.GSM2493775.RNAseq.RNAseq Atha.Col.0.PhyD.root.GC.dark.Rep3.GSM2493776.RNAseq.RNAseq Atha.Col.0.root.FLT.Alight.Rep1.GSM2493777.RNAseq.RNAseq Atha.Col.0.root.FLT.Alight.Rep2.GSM2493778.RNAseq.RNAseq Atha.Col.0.root.FLT.Alight.Rep3.GSM2493779.RNAseq.RNAseq Atha.Col.0.root.FLT.dark.Rep1.GSM2493786.RNAseq.RNAseq Atha.Col.0.root.FLT.dark.Rep2.GSM2493787.RNAseq.RNAseq Atha.Col.0.root.FLT.dark.Rep3.GSM2493788.RNAseq.RNAseq Atha.Ws.root.FLT.Alight.Rep1.GSM2493780.RNAseq.RNAseq Atha.Ws.root.FLT.Alight.Rep2.GSM2493781.RNAseq.RNAseq Atha.Ws.root.FLT.Alight.Rep3.GSM2493782.RNAseq.RNAseq Atha.Ws.root.FLT.dark.Rep1.GSM2493789.RNAseq.RNAseq Atha.Ws.root.FLT.dark.Rep2.GSM2493790.RNAseq.RNAseq Atha.Ws.root.FLT.dark.Rep3.GSM2493791.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.Alight.Rep1.GSM2493783.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.Alight.Rep2.GSM2493784.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.Alight.Rep3.GSM2493785.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.dark.Rep1.GSM2493792.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.dark.Rep2.GSM2493793.RNAseq.RNAseq Atha.Col.0.PhyD.root.FLT.dark.Rep3.GSM2493794.RNAseq.RNAseq
Sample.Id
Sample.Name Atha_Col-0_root_GC_Alight_Rep1_GSM2493759 Atha_Col-0_root_GC_Alight_Rep2_GSM2493760 Atha_Col-0_root_GC_Alight_Rep3_GSM2493761 Atha_Col-0_root_GC_dark_Rep1_GSM2493768 Atha_Col-0_root_GC_dark_Rep2_GSM2493769 Atha_Col-0_root_GC_dark_Rep3_GSM2493770 Atha_Ws_root_GC_Alight_Rep1_GSM2493762 Atha_Ws_root_GC_Alight_Rep2_GSM2493763 Atha_Ws_root_GC_Alight_Rep3_GSM2493764 Atha_Ws_root_GC_dark_Rep1_GSM2493771 Atha_Ws_root_GC_dark_Rep2_GSM2493772 Atha_Ws_root_GC_dark_Rep3_GSM2493773 Atha_Col-0-PhyD_root_GC_Alight_Rep1_GSM2493765 Atha_Col-0-PhyD_root_GC_Alight_Rep2_GSM2493766 Atha_Col-0-PhyD_root_GC_Alight_Rep3_GSM2493767 Atha_Col-0-PhyD_root_GC_dark_Rep1_GSM2493774 Atha_Col-0-PhyD_root_GC_dark_Rep2_GSM2493775 Atha_Col-0-PhyD_root_GC_dark_Rep3_GSM2493776 Atha_Col-0_root_FLT_Alight_Rep1_GSM2493777 Atha_Col-0_root_FLT_Alight_Rep2_GSM2493778 Atha_Col-0_root_FLT_Alight_Rep3_GSM2493779 Atha_Col-0_root_FLT_dark_Rep1_GSM2493786 Atha_Col-0_root_FLT_dark_Rep2_GSM2493787 Atha_Col-0_root_FLT_dark_Rep3_GSM2493788 Atha_Ws_root_FLT_Alight_Rep1_GSM2493780 Atha_Ws_root_FLT_Alight_Rep2_GSM2493781 Atha_Ws_root_FLT_Alight_Rep3_GSM2493782 Atha_Ws_root_FLT_dark_Rep1_GSM2493789 Atha_Ws_root_FLT_dark_Rep2_GSM2493790 Atha_Ws_root_FLT_dark_Rep3_GSM2493791 Atha_Col-0-PhyD_root_FLT_Alight_Rep1_GSM2493783 Atha_Col-0-PhyD_root_FLT_Alight_Rep2_GSM2493784 Atha_Col-0-PhyD_root_FLT_Alight_Rep3_GSM2493785 Atha_Col-0-PhyD_root_FLT_dark_Rep1_GSM2493792 Atha_Col-0-PhyD_root_FLT_dark_Rep2_GSM2493793 Atha_Col-0-PhyD_root_FLT_dark_Rep3_GSM2493794
GLDS 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
Accession GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120 GLDS-120
Hardware Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish
Tissue Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots
Age 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days 11 days
Organism Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana
Ecotype Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0
Genotype WT WT WT WT WT WT WT WT WT WT WT WT PhyD PhyD PhyD PhyD PhyD PhyD WT WT WT WT WT WT WT WT WT WT WT WT PhyD PhyD PhyD PhyD PhyD PhyD
Variety Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 WT Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD Col-0 PhyD
Radiation Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Background Earth Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation
Gravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity Microgravity
Developmental 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots 11 day old seedling roots
Time.series.or.Concentration.gradient Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point
Light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light 4-6 umoles m-2 s-1 total light Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown Light-activated for 4 hours, then dark-grown
Analysis.Light Alight Alight Alight Dark Dark Dark Alight Alight Alight Dark Dark Dark Alight Alight Alight Dark Dark Dark Alight Alight Alight Dark Dark Dark Alight Alight Alight Dark Dark Dark Alight Alight Alight Dark Dark Dark
Assay..RNAseq. RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling RNAseq Transcription Profiling
Temperature
Treatment.type
Treatment.intensity X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
Treament.timing X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
Preservation.Method. RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater 
 readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
   kable( head(readData.out$data) ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Col0_GC_Alight_Rep1 Col0_GC_Alight_Rep2 Col0_GC_Alight_Rep3 Col0_GC_dark_Rep1 Col0_GC_dark_Rep2 Col0_GC_dark_Rep3 Ws_GC_Alight_Rep1 Ws_GC_Alight_Rep2 Ws_GC_Alight_Rep3 Ws_GC_dark_Rep1 Ws_GC_dark_Rep2 Ws_GC_dark_Rep3 Col0PhyD_GC_Alight_Rep1 Col0PhyD_GC_Alight_Rep2 Col0PhyD_GC_Alight_Rep3 Col0PhyD_GC_dark_Rep1 Col0PhyD_GC_dark_Rep2 Col0PhyD_GC_dark_Rep3 Col0_FLT_Alight_Rep1 Col0_FLT_Alight_Rep2 Col0_FLT_Alight_Rep3 Col0_FLT_dark_Rep1 Col0_FLT_dark_Rep2 Col0_FLT_dark_Rep3 Ws_FLT_Alight_Rep1 Ws_FLT_Alight_Rep2 Ws_FLT_Alight_Rep3 Ws_FLT_dark_Rep1 Ws_FLT_dark_Rep2 Ws_FLT_dark_Rep3 Col0PhyD_FLT_Alight_Rep1 Col0PhyD_FLT_Alight_Rep2 Col0PhyD_FLT_Alight_Rep3 Col0PhyD_FLT_dark_Rep1 Col0PhyD_FLT_dark_Rep2 Col0PhyD_FLT_dark_Rep3
AT1G07590 14.09484 15.82643 16.00050 13.10404 13.51777 14.64386 13.409439 15.77315 14.82674 8.734572 12.853189 14.49060 14.30307 16.42429 16.25823 14.93434 14.023780 13.652232 13.06820 13.47644 15.85437 14.53257 12.546514 16.29378 13.70761 17.15691 15.84185 11.771773 15.17731 15.44411 15.63005 16.46416 16.53664 16.16638 14.22419 11.227882
AT1G21310 13.82237 12.89321 13.59194 12.69178 11.84638 13.57377 11.609280 14.87147 13.40320 10.803775 11.668910 12.93797 12.21651 15.52722 14.64023 14.21443 13.702426 13.487243 13.86935 13.89505 14.02481 14.15226 13.295761 15.67999 14.01774 15.40720 14.53493 13.029378 14.92947 14.70538 14.61414 13.92094 14.68044 14.58744 13.84959 11.987155
AT2G33830 13.00360 12.70793 13.19989 11.07625 11.44428 12.10500 11.605670 13.01332 13.23629 6.342765 8.041026 10.62547 13.12869 14.63728 13.86356 12.00093 9.571015 8.582187 13.84084 13.54377 14.43678 13.47765 11.086524 14.73928 13.32348 14.63064 13.90582 9.399696 12.33273 12.11427 14.45015 14.83492 14.74970 13.46973 13.62021 13.043726
AT1G07610 12.08104 14.30673 14.23944 10.40031 11.39977 12.30921 9.268934 12.67998 11.15489 4.495592 9.733672 10.63264 11.22988 14.58783 14.11140 12.85493 11.888352 11.502633 10.64115 11.46993 13.77730 12.79971 9.080162 14.60663 10.27736 13.91128 12.58584 8.510065 11.83719 12.25284 13.89512 14.26232 14.34338 13.98226 11.39071 6.943481
AT5G03240 13.20130 13.12291 13.32156 11.49666 11.57663 13.03192 12.702830 13.73541 14.16480 10.261950 10.374225 11.62274 13.07120 14.91590 14.53655 13.45543 11.608011 11.671690 12.73701 13.21551 13.74191 12.73065 10.975705 12.96365 12.31093 14.01545 13.06204 10.566490 12.53392 12.64681 13.26962 14.37130 14.68130 13.65752 12.14785 12.731019
AT2G45960 14.76921 14.16179 14.40573 12.98223 12.07059 13.29898 13.412024 14.52394 14.19108 11.907155 12.186793 12.66575 12.72364 15.00718 14.16692 13.92458 12.483819 12.222020 13.90170 13.63596 14.27782 13.88254 12.522675 13.61255 14.13872 13.88720 14.02430 11.971582 13.47262 13.65144 13.90907 13.79361 14.08793 13.30008 12.89654 12.072701 
 readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
 kable( readSampleInfo.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Gravity Variety Light
Col0_GC_Alight_Rep1 Terrestrial Col0 WT Alight
Col0_GC_Alight_Rep2 Terrestrial Col0 WT Alight
Col0_GC_Alight_Rep3 Terrestrial Col0 WT Alight
Col0_GC_dark_Rep1 Terrestrial Col0 WT Dark
Col0_GC_dark_Rep2 Terrestrial Col0 WT Dark
Col0_GC_dark_Rep3 Terrestrial Col0 WT Dark
Ws_GC_Alight_Rep1 Terrestrial WS0 WT Alight
Ws_GC_Alight_Rep2 Terrestrial WS0 WT Alight
Ws_GC_Alight_Rep3 Terrestrial WS0 WT Alight
Ws_GC_dark_Rep1 Terrestrial WS0 WT Dark
Ws_GC_dark_Rep2 Terrestrial WS0 WT Dark
Ws_GC_dark_Rep3 Terrestrial WS0 WT Dark
Col0PhyD_GC_Alight_Rep1 Terrestrial Col0 PhyD Alight
Col0PhyD_GC_Alight_Rep2 Terrestrial Col0 PhyD Alight
Col0PhyD_GC_Alight_Rep3 Terrestrial Col0 PhyD Alight
Col0PhyD_GC_dark_Rep1 Terrestrial Col0 PhyD Dark
Col0PhyD_GC_dark_Rep2 Terrestrial Col0 PhyD Dark
Col0PhyD_GC_dark_Rep3 Terrestrial Col0 PhyD Dark
Col0_FLT_Alight_Rep1 Microgravity Col0 WT Alight
Col0_FLT_Alight_Rep2 Microgravity Col0 WT Alight
Col0_FLT_Alight_Rep3 Microgravity Col0 WT Alight
Col0_FLT_dark_Rep1 Microgravity Col0 WT Dark
Col0_FLT_dark_Rep2 Microgravity Col0 WT Dark
Col0_FLT_dark_Rep3 Microgravity Col0 WT Dark
Ws_FLT_Alight_Rep1 Microgravity WS0 WT Alight
Ws_FLT_Alight_Rep2 Microgravity WS0 WT Alight
Ws_FLT_Alight_Rep3 Microgravity WS0 WT Alight
Ws_FLT_dark_Rep1 Microgravity WS0 WT Dark
Ws_FLT_dark_Rep2 Microgravity WS0 WT Dark
Ws_FLT_dark_Rep3 Microgravity WS0 WT Dark
Col0PhyD_FLT_Alight_Rep1 Microgravity Col0 PhyD Alight
Col0PhyD_FLT_Alight_Rep2 Microgravity Col0 PhyD Alight
Col0PhyD_FLT_Alight_Rep3 Microgravity Col0 PhyD Alight
Col0PhyD_FLT_dark_Rep1 Microgravity Col0 PhyD Dark
Col0PhyD_FLT_dark_Rep2 Microgravity Col0 PhyD Dark
Col0PhyD_FLT_dark_Rep3 Microgravity Col0 PhyD Dark 
 input_selectOrg ="NEW" 
 input_selectGO <- 'GOBP'   #Gene set category 
 input_noIDConversion = TRUE  
 allGeneInfo.out <- geneInfo(geneInfoFile) 
 converted.out = NULL 
 convertedData.out <- convertedData()    
 nGenesFilter()  
## [1] "16156 genes in 36 samples. 16155  genes passed filter.\n Original gene IDs used."
 convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
 parDefault = par() 
 par(mar=c(12,4,2,2)) 
 # barplot of total read counts
 x <- readData.out$rawCounts
 groups = as.factor( detectGroups(colnames(x ) ) )
 if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
  col1 = rainbow(nlevels(groups))[ groups ]             
         
 barplot( colSums(x)/1e6, 
        col=col1,las=3, main="Total read counts (millions)")  

 readCountsBias()  # detecting bias in sequencing depth 
## [1] 0.05123677
## [1] 0.5460606
## [1] 0.2013552
## [1] 0.3019591
## [1] "No bias detected"
 # Box plot 
 x = readData.out$data 
 boxplot(x, las = 2, col=col1,
    ylab='Transformed expression levels',
    main='Distribution of transformed data') 

 #Density plot 
 par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
 densityPlot()       

 # Scatter plot of the first two samples 
 plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
    main='Scatter plot of first two samples') 

 ####plot gene or gene family
 input_selectOrg ="BestMatch" 
 input_geneSearch <- 'HOXA' #Gene ID for searching 
 genePlot()  
## NULL
 input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar? 
 geneBarPlotError()       
## NULL

3. Heatmap

 # hierarchical clustering tree
 x <- readData.out$data
 maxGene <- apply(x,1,max)
 # remove bottom 25% lowly expressed genes, which inflate the PPC
 x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
 plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

 #Correlation matrix
 input_labelPCC <- TRUE #Show correlation coefficient? 
 correlationMatrix() 

 # Parameters for heatmap
 input_nGenes <- 1000   #Top genes for heatmap
 input_geneCentering <- TRUE    #centering genes ?
 input_sampleCentering <- FALSE #Center by sample?
 input_geneNormalize <- FALSE   #Normalize by gene?
 input_sampleNormalize <- FALSE #Normalize by sample?
 input_noSampleClustering <- FALSE  #Use original sample order
 input_heatmapCutoff <- 4   #Remove outliers beyond number of SDs 
 input_distFunctions <- 1   #which distant funciton to use
 input_hclustFunctions <- 1 #Linkage type
 input_heatColors1 <- 1 #Colors
 input_selectFactorsHeatmap <- 'Light'  #Sample coloring factors 
 png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
 staticHeatmap() 
 dev.off()  
## png 
##   2

[heatmap] (heatmap.png)

 heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

 input_nGenesKNN <- 2000    #Number of genes fro k-Means
 input_nClusters <- 4   #Number of clusters 
 maxGeneClustering = 12000
 input_kmeansNormalization <- 'geneMean'    #Normalization
 input_KmeansReRun <- 0 #Random seed 

 distributionSD()  #Distribution of standard deviations 

 KmeansNclusters()  #Number of clusters 

 Kmeans.out = Kmeans()   #Running K-means 
 KmeansHeatmap()   #Heatmap for k-Means 

 #Read gene sets for enrichment analysis 
 sqlite  <- dbDriver('SQLite')
 input_selectGO3 <- 'GOBP'  #Gene set category
 input_minSetSize <- 15 #Min gene set size
 input_maxSetSize <- 2000   #Max gene set size 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO3,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 # Alternatively, users can use their own GMT files by
 #GeneSets.out <- readGMTRobust('somefile.GMT')  
 results <- KmeansGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 2.25e-169 236 Amide biosynthetic process
7.51e-167 225 Translation
1.76e-166 225 Peptide biosynthetic process
2.01e-161 313 Organonitrogen compound biosynthetic process
2.03e-159 242 Cellular amide metabolic process
4.21e-158 226 Peptide metabolic process
1.69e-89 137 Ribonucleoprotein complex biogenesis
5.02e-83 206 Cellular component biogenesis
1.94e-71 109 Ribosome biogenesis
1.93e-62 95 Response to cadmium ion
B 1.41e-14 17 Detoxification
2.90e-14 19 Response to toxic substance
2.90e-14 16 Drug catabolic process
2.90e-14 15 Cellular oxidant detoxification
3.83e-14 10 Water transport
3.83e-14 10 Fluid transport
3.83e-14 15 Cellular detoxification
4.93e-14 12 Hydrogen peroxide catabolic process
7.79e-14 15 Cellular response to toxic substance
2.35e-13 20 Response to oxidative stress
C 1.83e-32 129 Response to abiotic stimulus
2.31e-31 83 Response to inorganic substance
4.80e-25 54 Response to metal ion
1.10e-23 88 Cellular catabolic process
4.30e-23 96 Catabolic process
5.18e-23 45 Response to cadmium ion
5.68e-22 93 Response to oxygen-containing compound
7.79e-21 87 Oxidation-reduction process
9.68e-21 88 Cellular response to chemical stimulus
6.58e-19 74 Response to acid chemical
D 1.66e-16 51 Response to abiotic stimulus
1.66e-16 20 Cellular response to decreased oxygen levels
1.66e-16 43 Cellular response to chemical stimulus
1.66e-16 20 Cellular response to oxygen levels
1.66e-16 20 Cellular response to hypoxia
7.26e-16 38 Cellular response to stress
9.22e-16 20 Response to hypoxia
1.07e-15 20 Response to decreased oxygen levels
1.07e-15 20 Response to oxygen levels
2.58e-15 39 Response to external stimulus 
 input_seedTSNE <- 0    #Random seed for t-SNE
 input_colorGenes <- TRUE   #Color genes in t-SNE plot? 
 tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

 input_selectFactors <- 'Variety'   #Factor coded by color
 input_selectFactors2 <- 'Light'    #Factor coded by shape
 input_tsneSeed2 <- 0   #Random seed for t-SNE 
 #PCA, MDS and t-SNE plots
 PCAplot()  

 MDSplot() 

 tSNEplot()  

 #Read gene sets for pathway analysis using PGSEA on principal components 
 input_selectGO6 <- 'GOBP' 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO6,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

 cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Light (p=5.73e-03).
## PC2 is correlated with Variety (p=2.38e-06).
## PC5 is correlated with Gravity (p=1.07e-05).

6. DEG1

 input_CountsDEGMethod <- 2 #DESeq2= 3,limma-voom=2,limma-trend=1 
 input_limmaPval <- 0.1 #FDR cutoff
 input_limmaFC <- 2 #Fold-change cutoff
 input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial'  #Selected comparisons
 input_selectFactorsModel <- 'Gravity'  #Selected comparisons
 input_selectInteractions <- NULL   #Selected comparisons
 input_selectBlockFactorsModel <- NULL  #Selected comparisons
 factorReferenceLevels.out <- c('Gravity:Terrestrial','Light:Alight') 

 limma.out <- limma()
 DEG.data.out <- DEG.data()
 limma.out$comparisons 
## [1] "Microgravity-Terrestrial"
 input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
 input_UpDownRegulated <- FALSE #Split up and down regulated genes 
 vennPlot() # Venn diagram 

  sigGeneStats() # number of DEGs as figure 

  sigGeneStatsTable() # number of DEGs as table 
##                                       Comparisons Up Down
## Microgravity-Terrestrial Microgravity-Terrestrial 24   13

7. DEG2

 input_selectContrast <- 'Microgravity-Terrestrial' #Selected comparisons 
 selectedHeatmap.data.out <- selectedHeatmap.data()
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
 selectedHeatmap()   # heatmap for DEGs in selected comparison
## Error in selectedHeatmap(): object 'selectedHeatmap.data.out' not found
 # Save gene lists and data into files
 write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
 write.csv(DEG.data(),'DEG.data.csv' )
 write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
 input_selectGO2 <- 'GOBP'  #Gene set category 
 geneListData.out <- geneListData()  
 volcanoPlot()  

  scatterPlot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  MAplot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  geneListGOTable.out <- geneListGOTable()  
## Error in geneListGOTable(): object 'selectedHeatmap.data.out' not found
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO2,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_removeRedudantSets <- TRUE   #Remove highly redundant gene sets? 
 results <- geneListGO()  #Enrichment analysis
## Error in geneListGO(): object 'geneListGOTable.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 2.25e-169 236 Amide biosynthetic process
7.51e-167 225 Translation
1.76e-166 225 Peptide biosynthetic process
2.01e-161 313 Organonitrogen compound biosynthetic process
2.03e-159 242 Cellular amide metabolic process
4.21e-158 226 Peptide metabolic process
1.69e-89 137 Ribonucleoprotein complex biogenesis
5.02e-83 206 Cellular component biogenesis
1.94e-71 109 Ribosome biogenesis
1.93e-62 95 Response to cadmium ion
B 1.41e-14 17 Detoxification
2.90e-14 19 Response to toxic substance
2.90e-14 16 Drug catabolic process
2.90e-14 15 Cellular oxidant detoxification
3.83e-14 10 Water transport
3.83e-14 10 Fluid transport
3.83e-14 15 Cellular detoxification
4.93e-14 12 Hydrogen peroxide catabolic process
7.79e-14 15 Cellular response to toxic substance
2.35e-13 20 Response to oxidative stress
C 1.83e-32 129 Response to abiotic stimulus
2.31e-31 83 Response to inorganic substance
4.80e-25 54 Response to metal ion
1.10e-23 88 Cellular catabolic process
4.30e-23 96 Catabolic process
5.18e-23 45 Response to cadmium ion
5.68e-22 93 Response to oxygen-containing compound
7.79e-21 87 Oxidation-reduction process
9.68e-21 88 Cellular response to chemical stimulus
6.58e-19 74 Response to acid chemical
D 1.66e-16 51 Response to abiotic stimulus
1.66e-16 20 Cellular response to decreased oxygen levels
1.66e-16 43 Cellular response to chemical stimulus
1.66e-16 20 Cellular response to oxygen levels
1.66e-16 20 Cellular response to hypoxia
7.26e-16 38 Cellular response to stress
9.22e-16 20 Response to hypoxia
1.07e-15 20 Response to decreased oxygen levels
1.07e-15 20 Response to oxygen levels
2.58e-15 39 Response to external stimulus

STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.

 STRING10_species = read.csv(STRING10_speciesFile)  
 ix = grep('Arabidopsis thaliana', STRING10_species$official_name ) 
 findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
 findTaxonomyID.out  
## [1] 3702

Enrichment analysis using STRING

  STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
 input_STRINGdbGO <- 'Process'  #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro' 
 results <- stringDB_GO_enrichmentData()  # enrichment using STRING 
## Error in stringDB_GO_enrichmentData(): object 'selectedHeatmap.data.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 2.25e-169 236 Amide biosynthetic process
7.51e-167 225 Translation
1.76e-166 225 Peptide biosynthetic process
2.01e-161 313 Organonitrogen compound biosynthetic process
2.03e-159 242 Cellular amide metabolic process
4.21e-158 226 Peptide metabolic process
1.69e-89 137 Ribonucleoprotein complex biogenesis
5.02e-83 206 Cellular component biogenesis
1.94e-71 109 Ribosome biogenesis
1.93e-62 95 Response to cadmium ion
B 1.41e-14 17 Detoxification
2.90e-14 19 Response to toxic substance
2.90e-14 16 Drug catabolic process
2.90e-14 15 Cellular oxidant detoxification
3.83e-14 10 Water transport
3.83e-14 10 Fluid transport
3.83e-14 15 Cellular detoxification
4.93e-14 12 Hydrogen peroxide catabolic process
7.79e-14 15 Cellular response to toxic substance
2.35e-13 20 Response to oxidative stress
C 1.83e-32 129 Response to abiotic stimulus
2.31e-31 83 Response to inorganic substance
4.80e-25 54 Response to metal ion
1.10e-23 88 Cellular catabolic process
4.30e-23 96 Catabolic process
5.18e-23 45 Response to cadmium ion
5.68e-22 93 Response to oxygen-containing compound
7.79e-21 87 Oxidation-reduction process
9.68e-21 88 Cellular response to chemical stimulus
6.58e-19 74 Response to acid chemical
D 1.66e-16 51 Response to abiotic stimulus
1.66e-16 20 Cellular response to decreased oxygen levels
1.66e-16 43 Cellular response to chemical stimulus
1.66e-16 20 Cellular response to oxygen levels
1.66e-16 20 Cellular response to hypoxia
7.26e-16 38 Cellular response to stress
9.22e-16 20 Response to hypoxia
1.07e-15 20 Response to decreased oxygen levels
1.07e-15 20 Response to oxygen levels
2.58e-15 39 Response to external stimulus

PPI network retrieval and analysis

 input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis 
 stringDB_network1(1) #Show PPI network

Generating interactive PPI

 write(stringDB_network_link(), 'PPI_results.html') # write results to html file 
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")

## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")

## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
 browseURL('PPI_results.html') # open in browser

8. Pathway analysis

 input_selectContrast1 <- 'Microgravity-Terrestrial'    #select Comparison 
 #input_selectContrast1 = limma.out$comparisons[3] # manually set
 input_selectGO <- 'GOBP'   #Gene set category 
 #input_selectGO='custom' # if custom gmt file
 input_minSetSize <- 15 #Min size for gene set
 input_maxSetSize <- 2000   #Max size for gene set 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_pathwayPvalCutoff <- 0.2 #FDR cutoff
 input_nPathwayShow <- 30   #Top pathways to show
 input_absoluteFold <- FALSE    #Use absolute values of fold-change?
 input_GenePvalCutoff <- 1  #FDR to remove genes 

 input_pathwayMethod = 1  # 1  GAGE
 gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  
   
 results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters  
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GAGE analysis: Microgravity vs Terrestrial statistic Genes adj.Pval
Down Ribonucleoprotein complex biogenesis -5.1963 438 1.8e-04
RNA modification -5.136 321 1.8e-04
Ribosome biogenesis -4.7341 343 9.3e-04
NcRNA metabolic process -4.5995 425 1.2e-03
Cellular response to DNA damage stimulus -4.5335 337 1.3e-03
DNA repair -4.4672 314 1.5e-03
NcRNA processing -4.3895 357 1.9e-03
Cell cycle process -4.174 396 4.0e-03
Up Cellular response to decreased oxygen levels 8.6526 176 8.5e-14
Cellular response to oxygen levels 8.6526 176 8.5e-14
Cellular response to hypoxia 8.6113 175 8.5e-14
Response to hypoxia 8.2759 197 4.0e-13
Response to decreased oxygen levels 8.2434 200 4.0e-13
Response to oxygen levels 8.2415 201 4.0e-13
Response to drug 4.929 482 1.3e-04
Response to toxic substance 4.8246 275 2.2e-04
Response to chitin 4.7321 104 3.9e-04
Response to oxidative stress 4.6963 379 3.3e-04
Root epidermal cell differentiation 4.43 115 1.4e-03
Antibiotic catabolic process 4.3307 78 2.4e-03
Plant epidermal cell differentiation 4.213 132 2.5e-03
Antibiotic metabolic process 4.198 188 2.5e-03
Trichoblast differentiation 4.1821 102 2.8e-03
Cellular response to toxic substance 4.1395 148 2.8e-03
Cellular response to ethylene stimulus 4.0484 157 3.7e-03
Anatomical structure maturation 4.013 151 4.1e-03
Ethylene-activated signaling pathway 3.9876 141 4.3e-03
Cell maturation 3.9236 96 5.6e-03
Trichoblast maturation 3.9236 96 5.6e-03
Root hair cell differentiation 3.9236 96 5.6e-03 
 pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
 input_pathwayMethod = 3  # 1  fgsea 
 fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
 results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GSEA analysis: Microgravity vs Terrestrial NES Genes adj.Pval
Down Flavonoid metabolic process -2.0281 68 6.8e-03
Flavonoid biosynthetic process -2.0215 50 6.8e-03
Up Cellular response to decreased oxygen levels 2.5003 176 4.8e-03
Cellular response to oxygen levels 2.5003 176 4.8e-03
Cellular response to hypoxia 2.4957 175 4.8e-03
Response to hypoxia 2.4312 197 4.8e-03
Response to decreased oxygen levels 2.4214 200 4.8e-03
Response to oxygen levels 2.4194 201 4.8e-03
Trichoblast differentiation 2.3377 102 4.8e-03
Root epidermal cell differentiation 2.3336 115 4.8e-03
Cell maturation 2.261 96 4.8e-03
Trichoblast maturation 2.261 96 4.8e-03
Root hair cell differentiation 2.261 96 4.8e-03
Plant epidermal cell differentiation 2.2289 132 4.8e-03
Root hair cell development 2.1785 78 4.8e-03
Anatomical structure maturation 2.1676 151 4.8e-03
Antibiotic catabolic process 2.1537 78 4.8e-03
Root hair elongation 2.1266 58 4.8e-03
Developmental maturation 2.1243 165 4.8e-03
Response to chitin 2.1079 104 4.8e-03
Hydrogen peroxide catabolic process 2.0824 65 4.8e-03
Hydrogen peroxide metabolic process 2.0626 84 4.8e-03
Cellular response to toxic substance 2.0515 148 4.8e-03
Response to hydrogen peroxide 2.0447 65 4.8e-03
Ethylene-activated signaling pathway 2.0165 141 4.8e-03
Antibiotic metabolic process 2.0145 188 4.8e-03
Response to toxic substance 2.0106 275 4.8e-03
Cellular response to ethylene stimulus 2.0051 157 4.8e-03
Cellular detoxification 1.9879 137 4.8e-03
Drug catabolic process 1.9653 163 4.8e-03 
  pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

   PGSEAplot() # pathway analysis using PGSEA 
## Error in findContrastSamples(input_selectContrast1, colnames(convertedData.out), : object 'c.out' not found

9. Chromosome

 input_selectContrast2 <- 'Terrestrial-Microgravity'    #select Comparison 
 #input_selectContrast2 = limma.out$comparisons[3] # manually set
 input_limmaPvalViz <- 0.1  #FDR to filter genes
 input_limmaFCViz <- 2  #FDR to filter genes 
 genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion

10. Biclustering

 input_nGenesBiclust <- 1000    #Top genes for biclustering
 input_biclustMethod <- 'BCCC()'    #Method: 'BCCC', 'QUBIC', 'runibic' ... 
 biclustering.out = biclustering()  # run analysis

 input_selectBicluster <- 1 #select a cluster 
 biclustHeatmap()   # heatmap for selected cluster 

 input_selectGO4 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO4,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.9e-120 192 Peptide metabolic process
1.9e-117 182 Translation
3.3e-117 182 Peptide biosynthetic process
8.8e-113 185 Amide biosynthetic process
2.1e-111 196 Cellular amide metabolic process
5.5e-96 238 Organonitrogen compound biosynthetic process
1.4e-68 151 Response to inorganic substance
6.0e-57 101 Response to metal ion
1.3e-53 86 Response to cadmium ion
4.4e-49 197 Response to abiotic stimulus

11. Co-expression network

 input_mySoftPower <- 5 #SoftPower to cutoff
 input_nGenesNetwork <- 1000    #Number of top genes
 input_minModuleSize <- 20  #Module size minimum 
 wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.7780  1.540          0.918  368.00    378.00  520.0
## 2      2   0.2370  0.285          0.681  189.00    190.00  333.0
## 3      3   0.0936 -0.126          0.651  114.00    108.00  234.0
## 4      4   0.5360 -0.387          0.797   74.60     67.20  176.0
## 5      5   0.7270 -0.553          0.905   52.10     44.20  140.0
## 6      6   0.8430 -0.724          0.971   38.10     30.50  116.0
## 7      7   0.8550 -0.824          0.955   28.80     21.40   98.1
## 8      8   0.9000 -0.922          0.984   22.40     15.90   84.7
## 9      9   0.9090 -0.995          0.969   17.80     12.00   74.2
## 10    10   0.9080 -1.060          0.953   14.40      9.25   65.6
## 11    12   0.9290 -1.130          0.950    9.86      5.67   52.5
## 12    14   0.9370 -1.180          0.941    7.06      3.53   43.0
## 13    16   0.9550 -1.210          0.949    5.24      2.34   35.8
## 14    18   0.9530 -1.220          0.940    4.00      1.54   30.2
## 15    20   0.9730 -1.230          0.965    3.12      1.08   25.8
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
 softPower()  # soft power curve 

  modulePlot()  # plot modules  

  listWGCNA.Modules.out = listWGCNA.Modules() #modules
 input_selectGO5 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO5,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_selectWGCNA.Module <- 'Entire network'   #Select a module
 input_topGenesNetwork <- 10    #SoftPower to cutoff
 input_edgeThreshold <- 0.4 #Number of top genes 
 moduleNetwork()    # show network of top genes in selected module
##  softConnectivity: FYI: connecitivty of genes with less than 12 valid samples will be returned as NA.
##  ..calculating connectivities..

 input_removeRedudantSets <- TRUE   #Remove redundant gene sets 
 results <- networkModuleGO()  #Enrichment analysis of selected module
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.9e-120 192 Peptide metabolic process
1.9e-117 182 Translation
3.3e-117 182 Peptide biosynthetic process
8.8e-113 185 Amide biosynthetic process
2.1e-111 196 Cellular amide metabolic process
5.5e-96 238 Organonitrogen compound biosynthetic process
1.4e-68 151 Response to inorganic substance
6.0e-57 101 Response to metal ion
1.3e-53 86 Response to cadmium ion
4.4e-49 197 Response to abiotic stimulus